There is a significant body of work related to decision processing that can be used to improve decision processing computer systems. See, e.g., T. L. Saaty, A Scaling Method for Priorities in Hierarchical Structures, 15 J. Math Psychology 234 (1977); P. T. Harker & L. G. Vargas, Theory of Ratio Scale Estimation: Saaty's Analytic Hierarchy Process, 33 Management Science 1383 (1987); F. Zahedi, The Analytic Hierarchy Process—a Survey of the Method and Its Applications, 16 Interfaces 96 (1986); T. L. Saaty, The Analytic Hierarchy Process (1980) (hereinafter Saaty 1980); E. H. Forman, Decision Support for Executive Decision Makers, 1 Information Strategy: The Executives Journal, Summer 1985; P. T. Harker, Alternative Modes of Questioning in the Analytic Hierarchy Process, 9 Math Modeling 353 (1987); Forman, E. H., Saaty, T. L., Selly, M. A., & Waldron, R., Expert Choice, Decision Support Software, McLean, Va., 1983; M. Gondran, M. Minoux, Graphs and Algorithms, Search for the connected component containing the vertex algorithm, at 15; T. L. Saaty, Decision Making for Leaders, 1995/1996 Edition, RWS Publications, Pittsburgh, Pa. (1985); and T. L. Saaty, Fundamentals of Decision Making and Priority Theory, Vol. 6, RWS Publications, Pittsburgh, Pa. (1994). All of the above publications are incorporated herein by reference.
Computer-based decision making is preferably hierarchy-based. For example, the Analytic Hierarchy Process (AHP) is a widely used method for decision making, as well as for prioritization and forecasting. For example, construction of a decision hierarchy can be achieved by utilizing a process of identifying pros and cons, then converting the pros and cons into objectives (see U.S. Pat. No. 5,995,728, incorporated herein by reference).
A constant and inherent problem in computer-based decision making is converting raw data to accurate and specific value scores that represent the inherent perceptions of relative value as held by all the associated and participating users. This is a task that has previously required constant and extensive human intervention, which resulted in the application of arbitrary scales and biased conversions, leading to tainted results with value scores that were essentially no more accurate than if they had been created using a random number generator. The other equally difficult and consistently inaccurate aspect of computer-based decision making that required constant human intervention, and that brought with it unjustified inputs and structural bias, is the capture and conversion of individuals or group judgments in order to derive priorities.